Epidemiology is often described as the cornerstone science in public health. Epidemiology in public health practice uses study design and analyses to identify causes in an outbreak situation, guides interventions to improve population health, and evaluates programs and policies.
In this course, we'll define the role of the professional epidemiologist as it relates to public health services, functions, and competencies. With that foundation in mind, we'll introduce you to the problem solving methodology and demonstrate how it can be used in a wide variety of settings to identify problems, propose solutions, and evaluate interventions. This methodology depends on the use of reliable data, so we'll take a deep dive into the routine and public health data systems that lie at the heart of epidemiology and then conclude with how you can use that data to calculate measures of disease burden in populations.

Taught By

Keri Althoff

Associate Professor

Transcript

Moving on to medical records systems as another example of routine health information systems. Medical record data can be used beyond estimation of vital statistics. Medical record systems, however, exist for clinical management of patients, not to collect data for use in solving public health problems. Medical record systems contain lots of different types of information. Including free text notes from physicians and lab values. Test and prescribed medications as well as rule out diagnoses. Extracting information on morbidity, deaths, and symptoms prior to death is not always a simple task and it may take lots of time and effort for both paper and electronic medical records. Other challenges include the need for validating outcomes with external sources, matching deaths with the death registry such as the National Death Index in the US. Or confirming a cancer diagnosis in the medical record with the Cancer Registry validates those important outcomes. Changes in diagnosis or procedural codes must be considered when abstracting data. There are a number of medical record systems used and even a number of electronic medical record systems used. So if you want to collect data across systems it may be very challenging to harmonize across those systems. Although electronic medical records may allow for easier logistics to access and abstract the data, it's important to remember that these systems were developed with the focus not only on patient care, but also for billing for medical services. The electronic medical record was not developed for data abstraction to better identify or address public health problems. Finally, if electronic medical record systems are not used there maybe issues with legibility of hand writing and missing records. For their paper records almost guarantees that data obstructors and data entry tools will be needed to curate the data you may want. Limitations to medical record data that can be abstracted from medical record systems include things like a lack of behavioral data and variability in the timing in which information is collected. This is oftentimes seen with some things such as smoking status when attempting to abstract it from the medical record. A physician is not likely to note at every visit that the patient is still smoking. They may note it once, and then note when a change in smoking behavior occurs. Additionally, if an individual is receiving care elsewhere, you may not have access to that information. So you cannot assume that you have a full capture of the patient's health status. When using data for medical record systems, one must keep in mind that you are receiving data from a sub group of the population. More specifically, the sub group that is seeking and receiving healthcare. If you were trying to estimate the burden of diabetes in a population, for example, it's not a safe assumption to think you have captured all the diabetes diagnosis for a population through a medical record system. Estimates that have a denominator such as a proportion can be very tricky unless you restrict to the population that are captured in the medical record system. Which may not be your population of interest. We'll take a quick break before moving on to our last example of routine health information systems.

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